ArticleCancer immunology, immunotherapy : CII2024
Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- DSG2+ Cancer Stem Cells Co-Located With FAP+ Myofibroblasts in the Tumor Boundary That Determines the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Current Insights and Future Directions of Multiomic and Spatial-Omic Analysis in Non-Small Cell Lung Cancer.JCO precision oncology · 2026Review
- Immune checkpoint inhibitors for the treatment of solid tumors and lymphoma in the past 26 years (2000-2025).Journal of hematology & oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (ANN) systems to predict the response to sintilimab for sqNSCLC.
methodsFour ANN systems based on bulk RNA data to predict disease control (DC), immune DC (iDC), objective response (OR) and immune OR (iOR) were constructed and tested for patients with sqNSCLC treated with sintilimab. The mechanism exploration on the bulk and the spatial level were performed in patients from the ORIENT-3 study and the real world, respectively.
findingssqNSCLC patients with different responses to sintilimab showed each unique transcriptomic spectrum. Four ANN systems showed high accuracy in the test cohort (AUC of DC, iDC, OR and iOR were 0.83, 0.89, 0.93 and 0.94, respectively). The performance of ANN systems was better than that of linear model systems and showed high stability. The mechanism exploration on the bulk level suggested that patients with lower ANN system scores (worse response) had a higher ratio of immune-related pathways enrichment. The mechanism exploration on the spatial level indicated that patients with better response to immunotherapy had fewer clusters of both tumor and cytotoxicity T cell spots.
interpretationThe four ANN systems showed high accuracy, robustness and stability in predicting the response to sintilimab for patients with sqNSCLC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.